I read a piece this week from Will Leitch about 10 dark-horse MVP candidates for 2019. I love pieces like that I wanted to bring that similar idea to the fantasy landscape. Replacing “MVP” with “top 10 hitter” which is essentially first round was easy part, but I wasn’t sure what threshold would be worthwhile and highlight some players we’re not already full hyping.

There is only one top 10 hitter this year who had a 2018 ADP later than 66 (Ronald Acuña at 128) and only Christian Yelich (66, obviously) joined him outside the top 33, but I thought inside the top 100 was too easy. Or not necessarily “easy” because identifying the players who will jump into next year’s top 10 hitters is insanely tough once you get past pick 30 or so, but getting drafted within the top 100 is essentially a co-sign that you could surge into those top two rounds. I decided to use 150 as my starting point.

I’ve got 10 hitters currently going outside the top 150 capable of having that dream breakout season needed to make “the leap”. I’ll identify the evolution we could see in their skills to reach the lofty heights worthy pushing to the 1st-2nd round area. After writing them up, I’ll give a 600 PA projection of what the dream season could look like if it comes to fruition. This is a mix of their career performance, their skills profile, projections, and then some dream dust sprinkled on top to get them into the 90th+ percentile of their potential outcomes.

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Yesterday, I discussed 10 hitters with big HR/FB rates whose marks were actually validated by their xHR/FB rates. Comparing HR/FB rate to xHR/FB rate helps guide my 2019 Pod Projections. Now let’s flip it and check on the hitters who posted surprisingly low HR/FB rates, but that were actually validated by low xHR/FB rates.

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At first glance, Max Kepler had a very Max Kepler year. In several key categories, he was pretty much the same player he’s always been, which is to say that he once again came close to, but failed to achieve, league average offensive output:

Where it really counts, in wOBA and wRC+, Kepler has been consistent—but consistently underwhelming. Skimming over these results, one would be inclined to conclude that the Twins are still waiting for Kepler to break out.

But ask anyone in the Twins front office, and they’d likely say that Kepler broke out last season, beneath our noses. And indeed, looking under the hood, we find several reasons to reach that same conclusion for ourselves:

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One of the first steps you must take en route to completing a player projection is determining if the previous season’s performance was “for real”. We all use historical statistics as our baseline for future forecasts, and the Pod Projections are no different. How do we come up with a home run projection? There are a bunch of components driving that projection, one of which is the hitter’s HR/FB rate. We could use my xHR/FB rate equation to look back and help determine whether a hitter’s actual HR/FB rate was real. So let’s begin with the guys who posted high HR/FB rates that xHR/FB completely supported. Though the validation doesn’t automatically mean a repeat is in the cards, there’s certainly better odds than if the metric suggested great fortune was primarily behind the mark.

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I’m finally at the point where I need to start working in replacement level production into my projections. Today’s focus is on hitters will miss a set time frame or just assume will miss some time. I’m not going to work in players who will miss time here and there (e.g.the Ryan Braun special) for this or that nagging injury. Instead, I’m focusing on batters who can be DL’ed and someone else can take their place.

To evaluate these players, their time off needs to be determined (my goal today) and then replacement level stats can be added in for these off weeks. The replacement level stats will be an average of the available waiver wire batters. While these replacement level players aren’t great, they will provide some production until the rookie/injured/suspended player returns. With every league being unique, owners are going to need to find this talent level for their own league.

And remember, these are my estimates (link to on-going updates). Each owner should make their own adjustments for their own risk tolerance.

Injured with a known time frame

Gregory Polanco: He had shoulder surgery in mid-September and had a seven to a nine-month recovery time frame with a mid-April to mid-June return.

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It is projection churning season and today, I’m going to investigate hitters in the top-200 who have the largest variation in projected plate appearances. I’m trying to see who seems off and any adjustments I’d make to their projections.

Normally, I just use a plate appearance average of several unique sources for my projections. I don’t have time to adjust each player. For now, I’m using five sources who constantly update their projected playing time. One is FanGraphs but I’m not going to reveal the other four as I don’t want to debate their merits. More importantly, it’s tough to know for sure who is wrong and who is right. In most instances, a reasonable explanation can be drawn for any total. Besides the players with the larger variation, I’ve included my top-300 hitters at the article’s end with their plate appearance differences.

Note: I ignored catchers and will look at them in detail at a later date.

Eloy Jimenez
Range: 336
Standard Deviation: 238
Average: 446

I’m not surprised with Jimenez being divisive. I sort of expected projections to have him being promoted either in mid-April (extra year of service) or early June (miss July two cutoff). One source was extremely low with a sub-300 value increasing the range. For me, I’d split the possible callup dates and go with a 525 plate appearance total and adjust it as more news becomes available.

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On Tuesday, the Blue Jays signed Freddy Galvis to a one-year contract. This could be a signal that the Jays have given up on Devon Travis at second base, who was terrible both offensively and defensively last season, which would push Lourdes Gurriel Jr. to second, opening up shortstop for Galvis. Amazingly, Galvis has now been an every day player for four straight seasons, and yet has never posted a wOBA exceeding .298. Will a move to Toronto, playing half his games at the Rogers Centre, be the spark he needs to finally get that wOBA over .300? Let’s check the park factors.

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Yesterday, I identified and discussed five hitters who made surprise appearances near the top of the average fly ball distance leaderboard. Today, I’ll talk bottom dwellers. Let’s find out which surprising hitters found themselves bringing up the rear. Like I did with the leaders, I won’t include the hitter if he also appeared on the surprise barrels per true fly ball laggard list as well.

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The amount of stats available to analysts today is overwhelming. At least it’s getting that way with me. I’d prefer everything to be available to investigate an idea. But no one has the time to go over every single stat to see if a player has changed for the better or worse. I’m going to eliminate all but 10 stats to focus on those few that matter the most.

The key behind my analysis is to find if a pitcher or hitter has changed. A few dozen projection sets exist to set a talent baseline but once the season starts, most people want to throw them out (some even before the season) if a hitter is batting .150 or a pitcher has a 6.00 ERA. Players are human and creatures of habit so most won’t change. But some do and being able to focus on these few can help to find values.

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About a week ago, I shared the surprising hitters who finished amid the top tier in barrels per true fly ball rate, a metric I created that acts as one of the primary components of my xHR/FB rate equation. Another major component of the equation is average fly ball distance (Avg FB Dist), which isn’t typically discussed, as it’s not on the default Statcast leaderboard. So let’s find out which surprising hitters, who hit at least 30 fly balls, finished near the top in the metric. I also decided not to include any hitters that also appeared on the Brls/TFB surprise leaders list.

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